The invention relates to speech coding, such as for computerized speech recognition systems.
In computerized speech recognition systems, an acoustic processor measures the value of at least one feature of an utterance during each of a series of successive time intervals to produce a series of feature vector signals representing the feature values. For example, each feature may be the amplitude of the utterance in each of twenty different frequency bands during each of a series of 10-millisecond time intervals. A twenty-dimension acoustic feature vector represents the feature values of the utterance for each time interval.
In discrete parameter speech recognition systems, a vector quantizer replaces each continuous parameter feature vector with a discrete label from a finite set of labels. Each label identifies a prototype vector signal having one or more parameter values. The vector quantizer compares the feature values of each feature vector signal to the parameter values of each prototype vector signal to determine the best matched prototype vector signal for each feature vector signal. The feature vector signal is then replaced with the label identifying the best-matched prototype vector signal.
For example, for prototype vector signals representing points in an acoustic space, each feature vector signal may be labeled with the identity of the prototype vector signal having the smallest Euclidean distance to the feature vector signal. For prototype vector signals representing Gaussian distributions in an acoustic space, each feature vector signal may be labeled with the identity of the prototype vector signal having the highest likelihood of yielding the feature vector signal. This label may be selected from a ranked list of most likely prototypes. It is to be understood that a group of the closest prototypes (i.e., prototype vector signals having the smallest distance to the feature vector signal or the prototype vector signal having the highest likelihood of yielding the feature vector signal) is collectively referred to as a leaf, whereby the closest prototype in the leaf gives the likelihood for calculating the overall rank associated with the leaf. Further, the list of ordered closest leaves may be referred to as a rank vector or, simply, a rank.
For large numbers of prototype vector signals and sub-prototype vector signals (for example, several thousand), comparing each feature vector signal to each prototype vector signal consumes significant processing resources by requiring many time-consuming computations.